Integrating D-S evidence theory and multiple deep learning frameworks for time series prediction of air quality.

ABSTRACT: Accurate prediction of air quality time series data is helpful to identify and warn air pollution events in advance. Although the current air quality prediction models have made some progress in improving the accuracy of prediction, due to the impact of specific pollutants or complex meteorological conditions, these models still have the problems of low prediction accuracy, robustness and generalization ability in univariate prediction. In order to solve these problems, this study proposes a framework that integrates D-S evidence theory and a variety of deep learning models. The air quality data of three representative cities with climate characteristics in China are obtained and five indicators on air pollutants are collected. The preprocessed data are divided by time length to form short

SUBMITTER: Feng S 

PROVIDER: S-EPMC11836142 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature